Understanding AI's Full Value Chain Potential

𝗠𝗼𝘀𝘁 𝗰𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀 𝗯𝗲𝗹𝗶𝗲𝘃𝗲 𝘁𝗵𝗮𝘁 𝗔𝗜 𝗶𝘀 𝗮 𝘀𝘁𝗿𝗮𝗶𝗴𝗵𝘁 𝗽𝗮𝘁𝗵 𝗳𝗿𝗼𝗺 𝗱𝗮𝘁𝗮 𝘁𝗼 𝘃𝗮𝗹𝘂𝗲. The assumption: 𝗗𝗮𝘁𝗮 → 𝗔I → 𝗩𝗮𝗹𝘂𝗲 But in real-world enterprise settings, the process is significantly more complex, requiring multiple layers of engineering, science, and governance. Here’s what it actually takes: 𝗗𝗮𝘁𝗮 • Begins with selection, sourcing, and synthesis. The quality, consistency, and context of the data directly impact the model’s performance. 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 • 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴: Exploration, cleaning, normalization, and feature engineering are critical before modeling begins. These steps form the foundation of every AI workflow. • 𝗠𝗼𝗱𝗲𝗹𝗶𝗻𝗴: This includes model selection, training, evaluation, and tuning. Without rigorous evaluation, even the best algorithms will fail to generalize. 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻 • Getting models into production requires deployment, monitoring, and retraining. This is where many teams struggle—moving from prototype to production-grade systems that scale. 𝗖𝗼𝗻𝘀𝘁𝗿𝗮𝗶𝗻𝘁𝘀 • Legal regulations, ethical transparency, historical bias, and security concerns aren’t optional. They shape architecture, workflows, and responsibilities from the ground up. 𝗔𝗜 𝗶𝘀 𝗻𝗼𝘁 𝗺𝗮𝗴𝗶𝗰. 𝗜𝘁’𝘀 𝗮𝗻 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗱𝗶𝘀𝗰𝗶𝗽𝗹𝗶𝗻𝗲 𝘄𝗶𝘁𝗵 𝘀𝗰𝗶𝗲𝗻𝘁𝗶𝗳𝗶𝗰 𝗿𝗶𝗴𝗼𝗿 𝗮𝗻𝗱 𝗼𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗺𝗮𝘁𝘂𝗿𝗶𝘁𝘆. Understanding this distinction is the first step toward building AI systems that are responsible, sustainable, and capable of delivering long-term value.

Reading about AI updates on a Saturday night (thrilling, I know), I stumbled across a fact I didn't realize: the tiny Caribbean island nation of Anguilla will earn over $100 million from selling ".ai" domains. They were assigned the .ai domain name in 1995 - and they even admitted, it was by chance. Anguilla's windfall with AI is perhaps the simplest possible form of AI value capture: right place + right time + fortunate asset. Opportunities with AI for most (especially for manufacturers, distributors, and industrial leaders I work with) aren’t going to happen passively. After a few dozen strategy sessions and workshops about AI in the distribution industry, I've identified three capabilities organizations need to have to really create value with AI: 1️⃣ Technical Fluency: It's table stakes to understand key AI terms and updates: what "MCP" and "fine tuning" mean, the difference between 4o and o3. But great leaders are ahead: tracking current state capabilities, industry applications, tech trajectories the way traders follow market futures, and mapping why any of this matters (happy to share resources I use to stay ahead, just dm me) 2️⃣ Expand Possibility, Don't Just Automate Most companies start with AI by asking: "how can AI do our current work faster?" This misses the point entirely imo. AI can handle tasks that previously required human judgment, expertise, and reasoning. That means you can now do things that were impossible before, not just do existing things better. The question used to be "how do we automate this process with AI?" It's "what becomes possible now that AI can think through problems we used to handle manually?" 3️⃣ Angle-First Strategies: Everyone knows AI is going to impact the economy, but the most successful organizations I see right now are doing more than implementing AI at the application layer: they’re aligning directly with the massive global build-out of hyperscale data centers. They recognize precisely where market momentum and capital are flowing. By intentionally positioning themselves as suppliers of critical infrastructure, they're embedding themselves firmly in the foundational layer powering every AI application, capturing long-term value. This should be on every distributor's mind with the recent announcement of the White House’s America AI Action Plan (btw more on this soon - I have some thoughts of what distributors need to take away from this) The momentum around AI in our economy isn't going anywhere. But capturing that value requires more than enthusiasm. It demands rigor. The organizations mapping deliberate strategies around AI - from implementation to infrastructure - stand to gain ground. Those treating this as just another tech trend will find themselves squeezed out. Anguilla got lucky with .ai The rest of us need to be intentional with AI. (and no, pic is not from Anguilla, but it's somewhere in the background of where I'm pointing)

That’s the potential promise of artificial intelligence. VCs all want to invest in business models with a defendable “moat”. Companies that can acquire more data and more users in a positive feedback loop have the chance to blast beyond the competition and become nearly unassailable. “The next Google”, or “the next Facebook”, it is said, will be a company predicated on taking advantage of this dynamic. Winner takes all. I’ve heard it called a “self-feeding data ecosystem.” Ben Narasin of Canvas Ventures calls it “a proprietary data plume” – an apt phrase (see his interview in top comment). How Data Dominance Works 1. Acquire more users, customers or installs  2. This leads to more data  3. More data leads to more learning and more #AI applications  4. More learning and more AI applications lead to a better product 5. A better product that is widely known leads to acquiring more users 6. This leads to more data 7. (And on and on and on…) In a nutshell: -- More users, customers, installs >> More data >> More AI capability >> Better product >> More users, customers, installs But this isn’t just about acquiring data for data’s sake. Macy’s, Exxon Mobil, and Wells Fargo have access to vastly more data than most businesses that have ever existed – why aren’t they AI innovators? Valuable, Proprietary Data The flywheel of data dominance does not spin simply because a company has access to data – it is only specific kinds of data that matter. We might think “data dominance data” as having two traits: Valuable – It can enable beneficial outcomes for users or for business processes. -- Amazon collects data about everything its users do on its site. Which products get clicks? Which viewed products get added to cart? Which products added to cart get bought? Which patterns of purchases correlate (i.e. does buying backpacks lead to more purchases of notebooks)? Users provide Amazon with tons of proxies for user interest, and tons of longitudinal evidence of their purchase behavior. This is valuable data that allows Amazon to better prepare for demand, and allows Amazon to better recommend products to its users. Exclusivity and Access – Few other organizations have it, few organizations have access to as much of it. -- Facebook’s platform is unique, and the data it collects is exclusive to its platform alone. This is not much of a strength for a small company, but for a company Facebook’s size (i.e. the largest social network on Earth), it means a torrent of data that allows Facebook to customize its experiences for its users – allowing it to stay ahead of other social networks in terms of user growth and engagement time on the platform. (Full article in top comment)

As we start 2024, it is clear that generative AI isn't just reshaping industries; it's redefining the very fabric of market dynamics and value creation. The "Year Ahead 2024 Report" by UBS Wealth Management USA (Link: https://lnkd.in/dXxWsccu) provides a compelling framework for understanding this transformation. 📈 📊 The Innovation Value Chain Framework: Here is a table from the report, a framework that categorizes the different layers of market players and how they've historically created value through technological disruptions. It's a strategic guide to understanding where the most significant opportunities lie in the generative AI revolution. 🏆 A Trillion-Dollar Milestone: In 2023, we witnessed a watershed moment – the first AI company, NVIDIA, reached a market capitalization of $1 trillion! And NVIDIA is likely just the beginning; we can expect to see more giants emerging in this space. Or maybe it is going to be the usual suspects, the Magnificent Seven, that will benefit the most in the short term? 💡 Leaders from Disruption: The future's highest returns in both public and private equity markets will likely come from companies that can leverage new technologies to expand markets, displace incumbents, or drastically reduce costs. Identifying these "leaders from disruption" is crucial for amplifying long-term portfolio potential. 🔍 Understanding the Framework: The UBS framework divides the value chain into four parts: 1️⃣ Infrastructure and input providers 2️⃣ Hardware manufacturers 3️⃣ Operators and enablers 4️⃣ Application beneficiaries By examining how each layer has historically capitalized on technological shifts, we can better predict where the next wave of market leaders will emerge in the generative AI space. Which layer of the innovation value chain takes the lead in the commercial value creation? My guess is it will be the "Application Beneficiaries". Application beneficiaries are primarily encompassing industries focused on text generation, programming, image, and video generation. These beneficiaries aren't directly involved in creating AI technologies but leverage them to revolutionize their services and products. Looking at the table below, it is not hard to see who won the Internet and Mobile Internet Technology disruption value creation race. ChatGPT's human-like text generation capabilities illustrate just the tip of the iceberg. In the short term, these beneficiaries stand to gain substantially as they integrate AI "copilots" into office productivity software, harness AI analytics for deeper insights, and incorporate AI into image, video, and other enterprise applications Where do you see the most commercial success in 2024 in terms of productivity and value creation? How do you think companies will harness new technologies to fuel growth and disrupt markets? #aiadoption #AIstartup #seedfunding #angelinvesting #vcfund